# Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning

arXiv: https://arxiv.org/abs/2505.09304 · HTML: https://mlsanity.com/paper/2505.09304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 14 May 2025.

## Builds on (most cited first, 11 of 11)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems](https://mlsanity.com/paper/2403.10549/) (2024)
- [Noisy student-teacher training for robust keyword spotting](https://mlsanity.com/paper/2106.01604/) (2021)
- [Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention](https://mlsanity.com/paper/2005.03867/) (2020)
- [Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics](https://mlsanity.com/paper/2307.12660/) (2023)
- [Ultra-Low Power Keyword Spotting at the Edge](https://mlsanity.com/paper/2111.04988/) (2021)
- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [Boosting keyword spotting through on-device learnable user speech characteristics](https://mlsanity.com/paper/2403.07802/) (2024)
- [Domain Aware Training for Far-field Small-footprint Keyword Spotting](https://mlsanity.com/paper/2005.03633/) (2020)

## Built on by (most cited first, 1 of 1)

- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)

## Abstract (arXiv)

Keyword spotting (KWS) is a key component of smart devices, enabling efficient and intuitive audio interaction. However, standard KWS systems deployed on embedded devices often suffer performance degradation under real-world operating conditions. Resilient KWS systems address this issue by enabling dynamic adaptation, with applications such as adding or replacing keywords, adjusting to specific users, and improving noise robustness. However, deploying resilient, standalone KWS systems with low latency on resource-constrained devices remains challenging due to limited memory and computational resources. This study proposes a low computational approach for continuous noise adaptation of pretrained neural networks used for KWS classification, requiring only 1-shot learning and one epoch. The proposed method was assessed using two pretrained models and three real-world noise sources at signal-to-noise ratios (SNRs) ranging from 24 to -3 dB. The adapted models consistently outperformed the pretrained models across all scenarios, especially at SNR ≤ 18 dB, achieving accuracy improvements of 4.9% to 46.0%. These results highlight the efficacy of the proposed methodology while being lightweight enough for deployment on resource-constrained devices.